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Product Management Root Cause Analysis Question: Investigating Google Translate's accuracy drop for Asian languages

Asked at Google

15 mins

Why did Google Translate accuracy drop by 30% for Asian languages?

Data Analysis Problem Solving Technical Understanding Technology AI/ML Language Services
Google Root Cause Analysis Data Quality Machine Learning Localization

Introduction

The sudden 30% drop in Google Translate accuracy for Asian languages is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term fixes and long-term strategic implications.

Framework overview

This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.

Step 1

Clarifying Questions (3 minutes)

  • Looking at the specificity of the drop, I'm thinking there might be a recent change. Has there been any significant update to the translation model or data sources for Asian languages in the past month?

Why it matters: Recent changes often correlate with performance shifts. Expected answer: Yes, a model update was rolled out two weeks ago. Impact on approach: If confirmed, we'd focus on the update's specifics and rollback options.

  • Considering the scale of the drop, I'm wondering about data quality. Have we seen any changes in the volume or sources of our training data for Asian languages recently?

Why it matters: Data quality directly impacts translation accuracy. Expected answer: No significant changes in data sources, but volume has increased. Impact on approach: We'd investigate data quality and preprocessing if volume changes are confirmed.

  • Given the focus on Asian languages, I'm curious about regional factors. Are we seeing this drop consistently across all Asian languages or is it more pronounced in specific ones?

Why it matters: Helps narrow down if it's a language-specific or broader issue. Expected answer: The drop is more severe in Chinese and Japanese. Impact on approach: We'd focus on these languages and their unique characteristics.

  • Thinking about user feedback, I'm wondering about the nature of the errors. Have we seen a change in the types of translation errors reported by users?

Why it matters: Different error types point to different root causes. Expected answer: Yes, more reports of contextual misinterpretations. Impact on approach: We'd investigate context handling in the translation model.

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